How salty is that seawater? Ask the Aquarius satellite

Satellite measures ocean salinity to study circulation and the water cycle.

Some 400 miles above the Earth, NASA satellites are watching you. They’re not actually interested in you or anything you do, of course—they’re just making measurements of environmental variables like temperature or cloud cover. While most people equate space agencies like NASA or the ESA with the exploration of extraterrestrial destinations, a critical part of their mission is to study our own planet from the unique orbital point of view.

The advantages are obvious: rapid coverage of large areas using the same instrument and continuous data collection. But there are challenges, too. It’s not exactly cheap to get the instrument up there, and if a component breaks, it’s game over. Then there’s the not insignificant task of finding a way to measure the thing you’re interested in from hundreds of miles away.

One of the newest members of the Earth-observing club is Aquarius (along with its friends aboard the SAC-D satellite). Launched on June 20, 2011, the satellite is a collaborative effort between the US and Argentina. Its job? To map surface ocean salinity around the globe and improve our understanding of ocean circulation and the hydrologic cycle.

The Aquarius instrument consists of two main components. The actual salinity measurement is made by a microwave radiometer that surveys the radiation emitted by the ocean surface. Because salinity affects the electrical conductivity of ocean water, it changes the microwave emissions perceptibly.

If the sea surface was perfectly smooth and calm, that would be pretty straightforward. But because waves affect the way that the radiation is emitted, it’s necessary to account for the roughness of the sea. This is accomplished by using a radar scatterometer that bounces energy off the surface and measures how much returns directly to the satellite.

The funny looking visor on the front of the satellite (interactive schematic here) is a reflector that focuses the signal, which is funneled through three “feed horns." The satellite is on a Sun-synchronous orbit, meaning it orbits from pole to pole in a plane that rotates westward around the Earth. Simply put, wherever it passes over the surface, it’s roughly 6pm. The business end of the satellite is pointed toward the dark side of the Earth, away from the Sun. As a result, the solar panels at the rear of the spacecraft point backwards, and the dog cone collar on the front is meant to shade the Aquarius instruments.

The underbelly of the satellite houses other instruments hitching a ride on SAC-D, which were built by the French, Italians, Canadians, and Argentinians. This includes another microwave radiometer tuned to study atmospheric conditions and sea ice, an infrared sensor to spot wildfires and measure sea surface temperature, and a camera looking for fires and aurorae. Two French instruments have very different tasks—investigating how cosmic radiation affects electronics and scanning for space debris.

NASA/JPL

Schematic showing the instruments and major components of the SAC-D satellite.

NASA/JPL

Schematic showing the instruments and major components of the SAC-D satellite.

VAFB/Randy Beaudoin

Putting the finishing touches on the satellite before launch.

NASA/VAFB

Loading the SAC-D satellite into the Delta II rocket that was kind enough to give it a lift.

NASA/Bill Ingalls

Ignition of the Delta II rocket carrying the SAC-D satellite.

Norman Kuring/NASA Goddard

Behold: data on the ocean's salinity returned by the Aquarius satellite.

NASA/JPL/Caltech

Diagram of the strategy behind the SPURS project: Collect data. Lots of data.

Woods Hole Oceanographic Institution/Dave Franantoni

The Research Vessel Knorr.

Woods Hole Oceanographic Institution/Dave Franantoni

Instrumented moorings for long-term monitoring of the SPURS study area.

Woods Hole Oceanographic Institution/Dave Franantoni

Drifters and floats deployed to gather data throughout the SPURS study area.

Screenshot of real-time data viewer showing paths traveled by various instruments throughout the study area.

Oceanographers had planned ahead to make the most of this unique new data source. The Salinity Processes in the Upper Ocean Regional Study (SPURS) involves well-orchestrated data gathering cruises. (One cruise was blogged at NASA Earth Observatory’s “Notes from the Field.") Using the Aquarius data for large-scale context, the cruises measure ocean water properties at extremely high-resolution over a small study area.

The research vessels deploy a veritable armada of data collection tools. Many measurements are made by the ships’ crews themselves, of course, but they cast a much wider net by utilizing a number of automated vehicles, anchored instruments, drifters, and floats from the ARGO network. By swarming the study area, a coherent picture of ocean mixing from macro to micro is captured, which will provide oceanographers a treasure trove to sift through.

Between the SPURS project and Aquarius’ global data set, oceanographers hope to both answer some nagging questions and discover the unexpected. Monitoring salinity over time could help researchers better understand how humans interact with ocean processes and the water cycle, including the effects of changing climate. It could also improve forecasts of the El Niño Southern Oscillation, which affects weather patterns from year to year.

So once a week around 6pm, tip your hat to the salt-seeking eye in the sky as it streaks overhead. It won’t notice, but it’s the thought that counts.

Would be interesting to see if the fresh water (and its "swirls" /path) from the amazon had anything to do with hurricane formation or patches of increased/decreased salinity having an effect on weather and climate

"If the sea surface was perfectly smooth and calm, that would be pretty straightforward. But because waves affect the way that the radiation is emitted, it’s necessary to account for the roughness of the sea. This is accomplished by using a radar scatterometer that bounces energy off the surface and measures how much returns directly to the satellite."

Getting highly directional radar to work from that distance and at the speed a satellite travels is impressive. I'm wondering if the horn antenna must be pointed to lead itself slightly to account for the travel time of the satellite during the propagation delay of the backscatter?

I'm also curious about measuring RF emissions from the ocean. I wasn't aware that the sea actively emitted anything that would be detectable above thermal noise.

Getting highly directional radar to work from that distance and at the speed a satellite travels is impressive. I'm wondering if the horn antenna must be pointed to lead itself slightly to account for the travel time of the satellite during the propagation delay of the backscatter?

The spacecraft moves at 'only' 7 km/s, that's a fraction of the beams' footprint (about 100km). Two of the beams point slightly ahead, but the third one points slightly "behind".

I'm just fascinated to learn that there's such a thing as a 'scatterometer'.

The name is likely derived from the wave propagation mechanism known as 'scattering'.

Basically, when a radio wave impinges on a perfectly smooth surface, you get a 'reflected' component and a 'transmitted' component, the relative strength of each being determined by the conductivity properties of the material. This is analogous to the ray optics that you study in high school physics (angle of incidence = angle of reflection for the reflected component, etc.). A smooth surface fabricated out of a perfect conductor should reflect 100% of the incoming signal power and transmit 0% of it. This is why you would get no cell signal inside of a well constructed metallic box (the incoming signal cannot penetrate it) but you CAN get a decent signal inside of buildings with wooden or concrete walls (transmitted component is weak but detectable).

So, what happens when the reflecting surface is not smooth, as in the case of a rough sea? This leads to 'scattering' rather than 'specular reflection', wherein the reflected energy is scattered in many different directions, resulting in a much weaker signal being returned back to the satellite compared to the case of smooth sea.

Except for non-normal incidence angles, like here, where a perfectly smooth surface would return nothing towards the spacecraft and where more roughness means more signal coming back

Damn, I guess I hadn't thought it through clearly (for the second time ). Do the non-normal angles arise because they're trying to cover the entire sea during each orbit rather than doing basically one line at a time? Do they use some sort of physics-based statistical model for sea roughness which accurately predicts the average strength of the returned signal? You seem to know a lot about this jbart.

Damn, I guess I hadn't thought it through clearly (for the second time ). Do the non-normal angles arise because they're trying to cover the entire sea during each orbit rather than doing basically one line at a time?

I was not there at the genesis of the project, so I can't tell for sure, but I'd bet the main reason for the oblique angle is to point away from the sun-lit side of the Earth as much as possible (can't avoid it completely at high latitudes). The Sun is about 1000 times 'brighter' than the ocean at that frequency, so you'd do want to avoid it's reflection on the ocean surface toward the spacecraft ....But you are correct that there is also a coverage issue: with three beams (oblique) to the side (at different angles), you cover more ground during one pass.

Grieviant wrote:

Do they use some sort of physics-based statistical model for sea roughness which accurately predicts the average strength of the returned signal? You seem to know a lot about this jbart.

There are some physical-based statistical models for sea roughness (linking the sea 'power spectrum' 'to the wind speed, at the very least, but also to the wind stress, wave age, fetch, etc ... for the most sophisticated of them), but they have significant uncertainty. So one usually end up deriving a 'model function': one uses a large amount of the instruments data, fit the variation of the signal to wind speed and/or other statistical descriptors of the sea state obtained from ancillary sources (weather models, other sensors, etc ...). This is not very satisfying intellectually, and can poses some issues of self-confirmation bias, but that can work pretty well if done carefully. Also the point of the scatterometer here is to derive the impact of roughness on the radiometers' measurements, and to correct for it, not to actually retrieve roughness (or wind speed) itself; so an empirical relationship is acceptable. For scatterometers dedicated to measuring wind speed, that's a bit different.